{"id":"W1514904414","doi":"10.1609/socs.v2i1.18196","title":"Predicting Solution Cost with Conditional Probabilities","year":2021,"lang":"en","type":"article","venue":"Proceedings of the International Symposium on Combinatorial Search","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; University of Regina","keywords":"Heuristic; Consistent heuristic; Path (computing); Incremental heuristic search; Mathematical optimization; Computer science; Bidirectional search; Algorithm; Bounded function; Best-first search; Search algorithm; Beam search; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001697926,0.0006634779,0.0006605881,0.001382636,0.0004394268,0.001169865,0.0009799061,0.001024621,0.002821169],"category_scores_gemma":[0.02336109,0.0005566847,0.0004258326,0.001056524,0.0006742593,0.002547354,0.0007877462,0.001627662,0.0005704206],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001199581,"about_ca_system_score_gemma":0.002005874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007867999,"about_ca_topic_score_gemma":0.007975116,"domain_scores_codex":[0.9990625,0.0003002014,0.00004926586,0.000215544,0.0002626066,0.0001098828],"domain_scores_gemma":[0.9838213,0.01252178,0.0007936244,0.001214241,0.001401424,0.0002477526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000157879,0.0000668252,0.006571446,0.00004592343,0.00002127871,0.00002492997,0.00002769403,0.9341526,0.0008821932,0.003964726,0.00105146,0.05303302],"study_design_scores_gemma":[0.000003666841,0.00001062169,0.0003606276,0.000003011475,0.000002761085,0.000004048972,0.000003323214,0.997282,0.0004483439,0.001807359,0.00007143161,0.000002640006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2810035,0.0003330591,0.7101628,0.0005300115,0.00006542575,0.00009860788,0.0004683441,0.003212318,0.004125905],"genre_scores_gemma":[0.8575052,0.000114811,0.1407278,0.00006109429,0.00002057478,0.0001368281,0.0004737863,0.0001548662,0.0008051494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007867999,"threshold_uncertainty_score":0.01564437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576057319684234,"score_gpt":0.2408829389124096,"score_spread":0.2251223657155672,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}